code: def hf_bart(model, inputs):
      output_seq_len = inputs.size()[1]
      print("hf_bart input size:", inputs.size())
      print("output seq len", output_seq_len)
      inputs = inputs.to("cuda:0")
      torch.cuda.synchronize()
      start_time = time.perf_counter()
      for _ in range(20):
          generated_ids = model.generate(
              inputs,
              max_length=output_seq_len,
              min_length=output_seq_len,
              use_cache=True,
              early_stopping=False,
              num_beams=4,
          )
      torch.cuda.synchronize()
      end_time = time.perf_counter()
      print("output size:", generated_ids.size())
      return generated_ids, (end_time - start_time) / 20
  
file_path: lightseq/examples/inference/python/test/ls_bart_bench.py
imports: import time
  import argparse
  import torch
  import lightseq.inference
  from transformers import FSMTForConditionalGeneration, FSMTTokenizer
label_desc: Use image node_id 2 for calling a global function with example usage: hf_bart(model, inputs) and returns: generated_ids,  unknown